Development, Evaluation, and Application of Machine Learning Models for Accurate Prediction of Root Uptake of Per- and Polyfluoroalkyl Substances.
Environ Sci Technol
; 57(46): 18317-18328, 2023 Nov 21.
Article
en En
| MEDLINE
| ID: mdl-37186812
ABSTRACT
Machine learning (ML) models were developed for understanding the root uptake of per- and polyfluoroalkyl substances (PFASs) under complex PFAS-crop-soil interactions. Three hundred root concentration factor (RCF) data points and 26 features associated with PFAS structures, crop properties, soil properties, and cultivation conditions were used for the model development. The optimal ML model, obtained by stratified sampling, Bayesian optimization, and 5-fold cross-validation, was explained by permutation feature importance, individual conditional expectation plot, and 3D interaction plot. The results showed that soil organic carbon contents, pH, chemical logP, soil PFAS concentration, root protein contents, and exposure time greatly affected the root uptake of PFASs with 0.43, 0.25, 0.10, 0.05, 0.05, and 0.05 of relative importance, respectively. Furthermore, these factors presented the key threshold ranges in favor of the PFAS uptake. Carbon-chain length was identified as the critical molecular structure affecting root uptake of PFASs with 0.12 of relative importance, based on the extended connectivity fingerprints. A user-friendly model was established with symbolic regression for accurately predicting RCF values of the PFASs (including branched PFAS isomerides). The present study provides a novel approach for profound insight into the uptake of PFASs by crops under complex PFAS-crop-soil interactions, aiming to ensure food safety and human health.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Contaminantes Químicos del Agua
/
Fluorocarburos
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
Environ Sci Technol
Año:
2023
Tipo del documento:
Article
País de afiliación:
China